1-D Modeling

1-D Model Structure

A MATLAB-based 1-D mixed layer model was used to simulate the evolution of the physical water column properties between 1984 and 2007. The mixed layer is forced by observed winds, shortwave radiation, surface air and dew point temperatures and a mean pressure gradient force to drive a mean current. The salinity is relaxed to observations on a 5 day time scale. This allows salinity stratification to breakdown during weather-band fluctuations (e.g., storms) but preserves the monthly to seasonal evolution of the salinity field. Observed winds are translated to wind stresses using the bulk formulae of Large and Pond (1981). The heat flux calculations follow those used by Mountain et al. (1996) in their study of surface heat fluxes in the Gulf of Maine: Latent and sensible heat fluxes are calculated from the wind speed, air-sea temperature difference and the dew point temperature using bulk formulae of Friehe and Schmitt (1976). Longwave radiation losses are calculated using the Efimova formula as reported by Simpson and Paulson (1979). 45% of the incoming shortwave radiation is assumed to be photosynthetically available (Baker & Frouin 1987) and was attenuated in the water column assuming a background attenuation of 0.1 m-1 and shading by chlorophyll (Lorenzen 1972). The grid spacing is 5 m and mixing is calculated with the Mellor-Yamada 2.5 algorithm with a background diffusivity of 1×10-4 m2 s-1. The water column depth is set to 100m on the NSS and 150m for the Gulf of Maine.

The ecosystem model is an adaptation of the model of Stock and Dunne (2010) to a 1-D water column. The model structure is shown in Fig 1. It was designed to resolve the primary energy flows within the planktonic food web and was used by Stock and Dunne (2010) to analyze global patterns in primary production, mesozooplankton production, and export. The compartments represent a core set of functional groups with rudimentary size differentiation common in many ecosystem models. Nitrogen is set to 14 mmoles N m-3 at the bottom boundary for the NSS and 20 mmoles N m-3 for the GoM. A Monod growth model is used for nutrient-limited growth. The light dependence is modeled according to Geider et al. (1997) and allows for variable Chlorophyll to Carbon ratios. Zooplankton have a Holling type 2 response for a single prey type and are assumed to engage in abundance-based switching when multiple prey types are available. The primary difference between small and large phytoplankton is an order of magnitude difference in half-saturation constants for nutrient uptake. The primary difference for the zooplankton types is a decline in the maximum grazing rate with increased size.

Fig. 1. Biological model structure.

1-D Model Skill Assessment

We first assessed the model skill in simulating the physical environment from 1984 to 2007. The model computed SST closely matches with the satellite-derived SST (Fig. 2, top two plots) in the GoM (R2 = 0.972) and the NSS (R2 = 0.98). Further, the model computed MLD also agrees reasonably well with the observed MLD in the GoM (R2 = 0.637, bias = -4.5m) and the NSS (R2 = 0.547, bias = -7.5m) sites (Fig. 2, bottom two plots). The model computed daily surface chlorophyll concentrations were also compared with the SeaWiFS derived chlorophyll concentrations from 1998-2007, showing a significant correlation in the GoM (r = 0.401, P < 0.01) and the NSS (r = 0.459, P < 0.01) (Fig. 3, top panel). These correlation coefficients are similar to that between in-situ water-sample measured and SeaWiFS derived chlorophyll concentrations in the GoM (r = 0.524, P < 0.01, unpublished results). The modeled mean timing and magnitude of blooms also showed a good agreement with that of SeaWiFS (Fig.3, bottom two plots).

A more detailed comparison of the annual bloom peak timing shows that the model successfully reproduces most of the regional and interannual variability in SPB timing in the GoM and NSS (Fig. 4, top two plots). The model also captures the difference of FPB timing between two sites (earlier in the GoM than NSS), but has more difficulty in capturing the FPB timing within each site (Fig. 4, bottom two plots). This is largely due to the uncertainties involved in identifying relatively weak and noisy FPB peak timing signals (compared to SPBs) from the SeaWiFS time series using shifted Gaussian fit.